Dynamic Packet Routing

ABSTRACT

Dynamic packet routing based on fabric awareness information is presented. Networking nodes in a networking fabric observe environmental properties across the fabric. When differences in environment properties between portions of the fabric are detected, differences in power consumption costs for example, the fabric generates corresponding routing tables. The networking nodes can then route traffic in a manner that is sensitive to the environment properties, power consumption or the cost of power for example.

This application claims the benefit of priority to U.S. provisionalapplication 61/540,932 filed on Sep. 29, 2011. This and all otherextrinsic materials discussed herein are incorporated by reference intheir entirety. Where a definition or use of a term in an incorporatedreference is inconsistent or contrary to the definition of that termprovided herein, the definition of that term provided herein applies andthe definition of that term in the reference does not apply.

FIELD OF THE INVENTION

The field of the invention is network infrastructure technologies.

BACKGROUND

Networking infrastructures comprise computing devices capable ofreceiving packets of data and forwarding the packets to desireddestinations. Typically networks include multiple interconnectednetworking nodes, each node having ingress and egress ports throughwhich traffic flows according to one or more forwarding rules. Each nodeexists in a physical environment that affects the cost to keep the nodeactive, not the least of which is power consumption. Power consumptioncan increase when networking nodes must route a heavy traffic load.

Others have put forth some effort toward managing power consumption innetworks. For example, U.S. patent application publication 2010/0229016to Kodama et al. titled “Assessing Conditions of Power Consumption inComputer Network”, filed Dec. 11, 2009, describes collecting powerconsumption information from connecting device. The information is usedto present a manager with itemized power consumption on a unit-by-unitbasis.

Another example in a similar vein as Kodama includes U.S. Pat. No.7,783,910 to Felter et al. titled “Method and System for AssociatingPower Consumption of a Server with a Network Address Assigned to theServer”, filed Mar. 30, 2007. Felter takes a step further in analysis ofpower consumption by inspecting network traffic to determine a networkaddress, which can then be associated with a power outlet.

Yet another example includes U.S. Pat. No. 7,634,617 to Frietsch et al.titled “Determining Power Consumption in Networks”, filed Jul. 26, 2006.Frietsch also contemplates determining a total electric powerconsumption of a network of networked devices. Kodama, Felter, andFrietsch provide useful information, but fail to address managingconsumption or costs, especially in an environment where costs can varyfrom one networking node to another.

U.S. patent application 2011/0055611 to Sharma et al. titled “System forControlling Power Consumption of a Network”, filed Jun. 17, 2010, makesfurther progress by selecting a network configuration of networkingnodes that has the lowest power consumption. Interestingly, Sharmacontemplates that determining a network configuration having the lowestoverall power consumption can include a particular network path fortransmitting network flow. However, Sharma also fails to appreciatemonetary costs can vary from one networking node to another where thecost variance can give rise to cost optimization. Further, thereferences above fail to appreciate that differences in networking nodeenvironments can be leveraged to generate dynamically adjustable routingpolicies to improve network operation.

Unless the context dictates the contrary, all ranges set forth hereinshould be interpreted as being inclusive of their endpoints andopen-ended ranges should be interpreted to include only commerciallypractical values. Similarly, all lists of values should be considered asinclusive of intermediate values unless the context indicates thecontrary.

Thus, there is still a need for networking nodes that dynamically adjustpacket routing policies.

SUMMARY OF THE INVENTION

The inventive subject matter provides apparatus, systems and methods inwhich a networking fabric can be constructed from networking nodescapable of dynamically adjusting their routing policies based on fabricwide environmental metrics. One aspect of the inventive subject matterincludes a networking node (e.g., a router, a switch, a gateway, anaccess point, etc.) having plurality of communication ports. Thecommunication ports can include wired, optical, wireless, or other typesof ports; physical or logical. Networking nodes can include a fabricawareness module capable of obtaining fabric awareness informationrepresenting aspects of the fabric environment. The fabric awarenessinformation can include fabric environment metrics across nodes of thefabric, and include differences in or among the metrics. The networkingnodes can further include a routing policy generator configured togenerate a routing policy for the node, possibly based on a policytemplate, as a function of the differences in the environmental metrics.As packet traffic passes through the networking node via thecommunication ports, a routing module forwards the packets according tothe generated policy and according to packet attributes. In especiallyinteresting embodiments, the network node is a member of ageographically distributed fabric where the cost of power varies by typeof power (e.g., solar, wind, coal, gas, etc.), by region (e.g., state,county, city, etc.), by time (e.g., summer, winter, day, night, etc.),or other factors. The networking node forwards packets to optimize acost factor based on the variances or differences in the metrics,possibly with respect to other networking metrics.

Various objects, features, aspects and advantages of the inventivesubject matter will become more apparent from the following detaileddescription of preferred embodiments, along with the accompanyingdrawing figures in which like numerals represent like components.

It should be noted that while the following description is drawn to anetworking infrastructure devices, various alternative configurationsare also deemed suitable and may employ various hardware-based computingdevices including servers, interfaces, modules, systems, databases,agents, peers, engines, controllers, or other types of computing devicesoperating individually or collectively. One should appreciate thecomputing devices comprise a hardware processor configured to executesoftware instructions stored on a tangible, non-transitory computerreadable storage medium (e.g., hard drive, solid state drive, RAM,flash, ROM, etc.). The software instructions preferably configure thecomputing device to provide the roles, responsibilities, or otherfunctionality as discussed below with respect to the disclosedapparatus. In especially preferred embodiments, the various servers,systems, databases, or interfaces exchange data using standardizedprotocols or algorithms, possibly based on HTTP, HTTPS, AES,public-private key exchanges, web service APIs, known financialtransaction protocols, or other electronic information exchangingmethods. Data exchanges preferably are conducted over a packet-switchednetwork, the Internet, LAN, WAN, VPN, or other type of packet switchednetwork.

The following discussion provides many example embodiments of theinventive subject matter. Although each embodiment represents a singlecombination of inventive elements, the inventive subject matter isconsidered to include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, then the inventive subjectmatter is also considered to include other remaining combinations of A,B, C, or D, even if not explicitly disclosed.

As used herein, and unless the context dictates otherwise, the term“coupled to” is intended to include both direct coupling (in which twoelements that are coupled to each other contact each other) and indirectcoupling (in which at least one additional element is located betweenthe two elements). Therefore, the terms “coupled to” and “coupled with”are used synonymously. Further, within the context of this document,“coupled with” and “coupled to” can be considered to include the conceptof two elements being communicatively coupled.

Networking nodes of a distributed fabric can be positioned nearlyanywhere in the world, or even in extraterrestrial locations, Earthorbit for example within satellites. Each networking node is disposedwithin a local environment having one or more properties that can affectperformance of the node. Each networking node's local environment canalso impact the performance of the entire fabric not just the networkingnode is self. Thus, there can be substantial differences between theenvironmental properties associated with one networking node in thefabric and the environmental properties associated with a secondnetworking node located elsewhere. Differences or variances among fabricmetrics give rise to possible optimizations that can have dramaticimpact on costs of maintaining the fabric at large.

Previous efforts appear to consider a network fabric as a singlestandalone entity rather than as an integrated system networkingelements where networking nodes can aggregate or disaggregate in tofederations providing services. Rather than viewing a fabric as astandalone entity, the Applicant has appreciated that systemperformance, system connectivity, system power consumption, systemmonetary cost, or other factors are highly relevant at different scales;from the global fabric level, through the federation level, or down tothe networking node level. Such factors also apply even down to theinternal component level; inter-core communications in a multi-coreprocessor as an example. For example, one can apply the disclosedtechniques to inter-core data forwarding within a multi-core processor.

Consider FIG. 1 , which presents average cost of electricity on astate-by-state basis in the United States. Networking nodes of anetworking fabric can be deployed in any of the states. If one were tooptimize traffic paths to minimize just power consumption, cost might bereduced somewhat. However, the actual monetary cost across the entirefabric would likely not be minimized because of difference in powercosts across the geo-graphically distributed fabric. More succinctly,power minimization does not necessarily result in cost optimization.Reducing power consumption by an amount in West Virginia has less thanone half the impact of reducing power in New York. Ideally, thenetworking nodes would adjust fabric operation to reduce cost, whilestill providing a quality of service rather than merely reducing asingle parameter such as power consumption. For large scale networkfabrics, the National Lambda Rail (see www.nlr.net) for example, spreadacross many states, the cost to operate the network can be quitesignificant due to the shear number of nodes and spread of the networkacross regions having expensive power costs.

FIG. 2 provides another example of cost variance where the cost ofdifferent types of electrical power varies by how it was generated. Thecosts presented in FIG. 2 are for the United States. However, one shouldappreciate that costs for types of electrical power can very accordingto other geo-political regions including a country, a state, a province,a county, a city, a town, a village, or other type of region. Still,further costs of electrical power can vary by time depending on when thepower is consume. For example, power costs can vary by season, month,week, day, time of day, or other temporal factors. Power costs or othermetrics can also vary due to influences from indirect factors:subsidies, licenses, relationships among regions or entities, or otherindirect factors. Such indirect factors can also be brought to bear indetermining routing policies for a fabric.

By constructing a fabric this is “self-aware” of its environment, thefabric can manage its roles or responsibilities to optimize itselfaccording to one or more fabric metrics. For example, the fabric canroute data traffic in a manner that reduces overall energy costs for thefabric as a whole. Local optimization does necessarily generate globaloptimization, especially in embodiments spanning across vast distances.Additionally, the fabric can route traffic according to the type ofpower used to power the local nodes. Such optimization can be applied toa globally or other geographically distributed fabric.

FIG. 3 presents ecosystem 100, which includes a possible embodiment fornetworking node 110. Networking nodes 110 can be members of a networkingfabric among other similarly enabled networking nodes 110. Networkingnodes 110 are similar in the sense they can each be configured tooperate according to the disclosed techniques. There is no requirementthat network nodes 110 be identical, fungible, or from even from thesame manufacturer.

Networking 110 nodes include a plurality of communication ports 113through which the networking node can exchange traffic packets with edgedevices or other networking nodes 110 possibly over network 115. Examplenetworking nodes 110 that can be suitability adapted to incorporate theinventive subject matter include Raptor Networking Technology switches,including the ER-1010 or other variants. Switches offered by othervenders, including Cisco®, Juniper®, Brocade®, or others could also beadapted to conform to the disclosed inventive subject matter. Othertypes of networking nodes can include routers, access points, deviceservers, print servers, game consoles, or other nodes capable ofreceiving or transmitting data packets. Networking nodes could alsoinclude inter-core routers within a multi-core processor.

Communication ports 113 can include wired ports, wireless ports, or evenoptical ports (e.g., HiGig, HiGig2, etc.). In some embodiments thenetworking ports 113 can include logical ports where a physical port canincorporate more than one logical data channel, possibly including anaddressable channel addressed by wavelength, frequency, signature, portidentifier, polarization pattern identifier, or other parameter. Alogical port is considered to represent at least one channel on aphysical port. For example, an optical fiber port can include multiplelogical ports where each logical port transports data at a differentwavelength of light (e.g., 850 nm, 1300 nm, 1550 nm, etc.) or viadifferent polarization states over a fiber coupled with the opticalfiber port.

Networking nodes 110 can comprise one or more sensor interfaces 117,possibly operating as an HTTP server or other suitable communicationinterface (e.g., serial interface, RS-232, RS-485, cellular, Bluetooth,Zigbee, 802.11, CAN, SPI, etc.), capable of acquiring environmentalproperties 160 for the local environment of nodes 110 from one or moresensors (e.g., thermometers, power meters, accelerometers, camera, GISsensors, user interfaces allowing administrator input, etc.). Eachnetworking node 110 can collect a digital representation of its localenvironment where the digital representation includes informationrelating power costs or other environmental properties 160. Sensorinterface 117 can include a direct connection to one or more sensors oran indirect connection through which environment properties 160 can beacquired. A direct connection could include one more sensors coupledwith the networking node 110, possibly internal or external sensors. Forexample, networking node 110 could include internal sensors that detecttemperature profiles, power consumption, traffic flow, processorutilization, memory utilization, storage capacity, or other internalproperties. Example external environment properties can include locationinformation (e.g., GPS, room, building, etc.), weather conditions, newsevents, or other information.

Sensors represent a broad spectrum of possible sensing equipmentcovering many modalities. Examples include acoustic or vibrationsensors, chemical sensors, electrical sensors, fluid sensors, weathersensors (e.g., barometer, humidity, etc.), radiation sensor, opticalsensors (e.g., camera, infrared, etc.), force sensors, proximitysensors, or other sensors. As an example, consider a scenario wherenetworking node 110 is liquid cooled. Networking node 110 can include afluid flow sensor monitoring the volume of liquid required to keepnetworking node 110 within a proper operating temperature profile.Environmental properties 160 can include a volume metric, which can thenbe mapped to a cost for maintaining a desired temperature. The cost canbe calculated via different techniques, perhaps simply based on a costof water assuming the liquid is water. Thus, environmental properties160 can represent a broad spectrum of data modalities, which can then bemapped to fabric environment metrics applicable to the environment,possibly at one or more scales (e.g., whole fabric, federation, region,group, rack, chassis, cores, etc.).

Networking nodes 110 preferably collect or aggregate environmentalproperties 160 and combine the environmental properties 160 into fabricawareness information. In some embodiments, networking nodes 110exchange fabric awareness information in the form of routing tables thatinclude fabric environment metrics representative of each networkingnode's local conditions. The routing tables can provide each networkingnode 110 a holistic view of routing paths through the entire fabric fromdifferent fabric environment metric perspectives. For example, therouting tables can be constructed to route traffic in a manner thatoptimizes a desired fabric environment metric; to power costs, resourceconsumption, or even more esoteric metrics such as human resourcemetrics representing the number of staff of a Network Operation Center(NOC) or representing cost to have an administrator to servicenetworking node 110 at a remote location.

The fabric awareness information can include differences amongnetworking node's 110 conditions or circumstances. One especiallyinteresting difference can include monetary cost of resources amonggeo-political regions. However, other differences are also contemplated,possibly including differences in temperature, type of energy costs,throughput, bandwidth, good-put, error counts, latency, or otherfactors. Once should note that differences do not necessarily require anarithmetic difference in a property metric, M, (e.g., M_(A)−M_(B))between two nodes, but could also include difference in metricinformation available at each node. For example, a difference in a costmetric for power represents an arithmetic difference in a property.Alternatively, if a first network node 110 has an environment propertyrepresenting water flow while a second node 110 does not, then the twonodes have a difference in available fabric metric information.

One should appreciate the differences in environmental metrics could beassociated with different levels of granularity. As discussedpreviously, differences could be due to geographic differences (see FIG.1 ) among nodes distributed over large geographic distances (e.g.,greater than 10 Km). Other differences could arise fromcampus-to-campus, building-to-building, room-to-room, rack-to-rack,chassis-to-chassis, or even down to internal component-to-internalcomponent in a chassis. Consider an example where fungible networkingnodes 110 are distributed across the continent where each node 110includes one or more multi-core processors. Each core could have rolesor responsibilities with respect to routing or switching traffic.Further, each core could have a different temperature profile that couldgive rise to differences in environmental metrics, which could bebrought to bear in how traffic is forwarded to control temperatureprofiles of the processors. Note such an approach does not require anactual monetary cost as a metric.

Each of networking nodes 110 can comprise a fabric awareness module 130that collects environment awareness metrics from other networking nodes110, possibly fungible nodes 110 of the fabric. Fabric awareness module130 can combine or otherwise aggregate the metrics into a form usable bynetworking node 110. The aggregated awareness metrics can be passed to arouting policy generator 140 within the network node. Although someembodiments allow for each networking node 110 to compile and processfabric awareness metrics, in some embodiments a single networking node110 operates as a fabric manager that governs the behavior of the entirefabric by generating desired routing behaviors from the collected fabricawareness metrics.

Routing policy generator 140 uses differences among the fabricenvironment metrics to generate a routing policy that can be applied tonetworking nodes 110 within the fabric. The routing policy can be basedone or more templates stored in database 120 and that includeprioritized criteria for forwarding traffic based on the metrics. Thecriteria can be populated according the actual metrics obtained from thefabric. The prioritized criteria comprise ranked rules that should beapplied based on packet attributes and on environmental metrics. Theranked rules can also include a listing of preferred routes possiblyorder by the fabric metrics. If a packet or metric lacks attributes thattrigger a first rule, then a second rule is checked until a routing ruleis found. A default rule within the prioritized criteria can be simply aroute based on a standard routing table. Such an approach allows eachnode to select a route that is considered to be sensitive to fabricoptimization.

Referring back to the scenario of cost of power consumption, each route,path, port, or other routing element can be weighted by the cost ofpower at each network node 110 along a route. These weightings can becombined with the template to derive a routing policy for the local node110 or even for remote networking nodes 110. One should appreciate thatother factors can also apply to populate the routing policy based on atemplate from database 120. Other factors can include urgency, latency,class of traffic, quality of service (QoS), bandwidth, load, weatherconditions, or other types of characteristics that can affectforwarding. Further, the factors can be based on packet attributesencoded within inter-frame headers, headers, or other portions of thepacket (e.g., IP address, port identifiers, etc.).

One should further appreciate the policies can be determined as afunction of the metrics beyond a straight one-to-one mapping of metricto policy rule. For example, cost of power can be balanced againstquality of service. Thus, a policy rule can be derived based on two ormore metrics. A corresponding policy could have a routing rule governedby a QoS metric among nodes 110 and a cost of power metric. Such a rulemight require that QoS be maintained within 95% of a baseline whiletraffic is routed through less power consumptive or costly paths, whichcould increase latency. Such prioritized routing rules can be considereddynamic in the sense that they can change with time. To continue the QoSexample, as traffic is routed to reduce power costs, the system can thenroute traffic to maintain QoS at the expense of cost to ensure desireduser experience. Such an approach is considered advantageous because itallows for global optimization of the entire fabric through ensuringdesirable behavior on average over time rather than requiring strictadherence to a rule at all points in time.

The populated routing policy can be submitted to one or more trafficrouting modules 150 having the responsibility for forwarding packetsfrom one communication port 113 to another. The routing module 150inspects the attributes of the packet to determine which rules withinthe policy pertain to the packet based on the priority criteria withinthe populated policy. Then the routing module forwards the packet outthe appropriate egress port 113 according to the policy's forwardingrules. Traffic routing module 150 can be configured to operate accordingto the nature of networking node 110. For example, in embodiments wherenetworking node 110 comprises a switch, traffic routing module 150 wouldbe configured to operate as a switch, possibly leveraging source ordestination physical switch port identifiers to route packets. Whennetworking node 110 comprises a router, traffic routing module 150 wouldbe configure to operate as a router where packets are routed based onnetwork addresses (e.g., URLs, transport layer ports, Internet protocoladdresses (IPv4, IPv6, etc.), etc.) within the packets, etc.). Oneshould appreciate that traffic routing module 150 be configured tooperate as a router, an access point, a gateway, a firewall, a proxy, aname server, a repeater, an exchange, a hub, or other type of networkingdevice.

In the circumstances where minimization of power cost is desirable, therouting policy could be configured to forward packets along paths have alow cost per power per packet metric. For example, each link to node 110could be weighted as one divided by the cost (1/cost), where a high costhas a lower weight. One should note that the reciprocal cost is a simpleweighting, while all cost-based weightings are contemplated. The routingpolicy can also fold in other parameters possibly as counter weights asdiscussed briefly with respect to QoS. For example, an urgent packet, orset of packets, can override the power consumption cost to achieveminimal latency. Real-time medical imaging supporting a surgeon insurgery might require low latency regardless of the cost, while anoff-line genomic analysis might not have such strict latencyrequirements. The genomic analysis might require low monetary costs datatransport or computation because it would be acceptable to receive theresults seconds or even hours late.

FIG. 4 presents an example where networking nodes 210 of fabric 240 aredistributed over vast geographic distances. In the example shown, onenetworking node among other networking nodes 210 operates as a fabricmanager 220. One should appreciate that any networking node 210 couldoperate as fabric manager 240. Fabric manger 240 collects the fabricawareness information from other networking nodes 210 where the fabricawareness information can be compiled in the form of one or more tables230. For example, fabric awareness tables 230 could be supplied via XMLtables, raw data, database files, CSV files, or other forms.

Tables 230 can comprise information relating to fabric 240 as a whole orportions (e.g., federations, regions, etc.) thereof, even down to acomponent level. Information in tables 230 can be stored using knowntechniques. For example, one table 230 can include power cost metricsassociated with use of each port on each networking node event to thepacket level. Each port could have a different per packet cost due tothe nature of each port. Some ports might use optical fiber transceiverswhile others might use Ethernet PHYs to generate signals where the powerconsumption for each type of port would be different.

Each table 230 can be considered a layer or view of the fabric awarenessinformation. For example, tables 230 could include capacity-weightedtables, thermal profile weighted tables, or other types of tables.Tables 230 can be considered somewhat orthogonal views of possiblefabric awareness information where each view has corresponding routingtables. Each “view” can be combined with each other as desired to form arouting policy according to defined templates. Fabric manger 220constructs tables 230 and can disseminate fabric awareness tables 230 toother networking nodes as desired or as necessary. When nodes 210 havefabric wide awareness, they can then route packets according to therules required in tables 230 in a manner that is sensitive to thecorresponding metric view.

Each fabric awareness tables 230 can include routing tables listing allpossible routes by which traffic can be routed across fabric 240 toensure the corresponding metrics are optimized. The routes listed intable 230 can be ranked or order as one or more preferred routesaccording the metric or metrics associated with the table. Each node 210can leverage the preferred routes according to their local awarenessmetric, which could override global policies. For example, one of tables230 can include a global power cost optimization, which lists two routesas preferred routes from coast-to-coast. However, an intermediary node210 might have severe congestion on a port along the most preferredroute. In response, the intermediary node 210 can take a less preferredroute to avoid local congestion.

In some embodiments networking nodes 210 can be fungible with respect tomanagement functions represented by fabric manager 220. Therefore, eachnode having management capabilities can also aggregate information fromother nodes to build local fabric awareness information tables 230 ormetrics. Again, such an approach is advantageous because fabric 240 canoptimize power consumption at different scales. Such an approach givesrise to a fractal fabric metric optimization structure. Further,networking nodes 210 can originate from different manufacturers as alonghas the core roles or responsibilities are incorporated in thenetworking nodes. One aspect of the inventive subject matter isconsidered to include constructing or management of fabric optimizationrouting standards according to which third party manufacturers implementtheir specific networking nodes 210 (e.g., routers, switches, gateways,repeaters, access points, etc.).

As traffic is forwarded from one node 210 to another, each node 210 canderive one or more packet attributes from arriving packets. Theattributes are compared against the generated routing policy stored onnode 210 and that operates based on the fabric awareness metrics orother information. Through the comparison, node 210 can determine how toroute the traffic to a next node so that the routing decision adheres tothe node's stored routing policy. For example, a packet could includefabric ingress/egress port identifiers of the originating source port orultimate destination port for the packet, possibly ports correspondingto edge devices located on opposite sides of the continent and locatedon different networking nodes 210. Node 210 can consult the routingpolicy, possibly constructed based on one or more layers in the fabricawareness information, to determine which local ports should be use tosend out the packet. One should note that the system does notnecessarily have to follow established routing paths. Rather, each node210 can determine how best to forward a packet based on its availableinformation as long as the decisions to forward the packets converge ona final destination while taking into other fabric environment metrics;monetary costs for example.

An astute reader will appreciate an environment that allows networkingnodes 210 to make their own decisions on how to forward packets couldresult in fragmentation of traffic. Therefore, one aspect of theinventive subject includes consideration of which path combinationsoptimize the reassembly of various types of disaggregatedtraffic—whether that traffic might be for encryption, for disaggregatedmanagement, or for file size considerations (i.e., where a single pathmight exceed the residual bandwidth available for massive filetransfers). Such an approach would address embodiments where data setsize could be extremely large, a person's genome for example, when sentto a point of care. The techniques disclosed in co-owned U.S. Pat. No.7,548,545 to Wittenschlaeger titled “Disaggregated Network Management”,filed May 13, 2008, can be leveraged to handle such scenarios.

Contemplated routing policies can include forwarding criteria thatdepend one or more of the fabric environmental metrics or packetattributes. Operational savings (e.g., performance, monetary costs,etc.) can be achieved when differences arise among the fabricenvironment metrics from one node 210 to another. When a node 210detects a change in fabric environment metrics, possibly in real-time,the node 210 can update its routing policies accordingly taking intoaccount the newly acquire information. In some embodiments, thedifferences in a metric, monetary cost for example, can represent adominate parameter in the forwarding criteria relative to other metrics,latency for example. The routing policies can be based on a templatehaving priorities on how to forward packets. For example, depending onthe packet type, class, or other attribute, node 210 can select anappropriate prioritized forwarding rule as discussed previously.

The inventive subject matter can be applied beyond packet routing per seand can also apply to distributed computation. In some embodiments,networking fabric 240 comprises a distributed computation fabric whereeach networking nodes 210 (e.g., switch, router, etc.) is configured tosupport general purpose computing applications. The networking nodes canalso construct one or more computation topologies to optimize one ormore fabric environment metrics to align with a distributedapplication's purpose. For example, the disclosed techniques can becombined with the techniques disclosed in co-owned U.S. patentapplication having Ser. No. 12/849,521 filed Aug. 3, 2010, titled“Hybrid Transport—Application Network Fabric Apparatus” (see U.S. patentapplication publication 2010/0312913) to achieve greater efficiencies inconstructing computation or routing topologies.

Yet another aspect of the inventive subject matter includes provisioningnetworking nodes 210 to take on a desired fabric environmental metricprofile. Referring back to monetary cost, the distributed fabric manager220 could instruct various nodes 210 to power down or enter ahibernation mode to reduce costs across the fabric until a desired costprofile has been achieved, at least to within acceptable thresholds.Another example includes instructing nodes 210 to align with atemperature profile. The temperature profile can require nodes 210 toshift locations of processing or power utilization to control hot spotlocation. The hot spots could be geographically distributed, distributedover a rack, within a chassis, or even within a multi-core processor. Asan illustrative example consider a rack of switches, the rack can housemulti-core switches operating as networking nodes 210 and could beinstructed to shift their computations to distribute thermal load: evennumbered chassis could shift processing to the right side of theirchassis while odd numbered chassis could shift processing to the leftside of the their chassis to distribute heat. The inventive subjectmatter is considered to include provisioning nodes 210 to take onspecific environmental metric profiles at various granularities fromfabric wide granularity down to an internal component level within oneor more chassis (e.g., cores in a single processing element).

Power cost savings for large scale network fabrics can be quite dramaticwhen the disclosed techniques are utilized to manage the fabric. In suchembodiments, the fabric environment metrics compiled by fabric manager220 can include one or more monetary costs associated with geo-politicalregions. Example monetary costs can include energy cost for the region,types of energy costs, average costs, variance of energy costs, or otherforms of monetary costs. Although energy costs represent one type ofvery interesting costs, other costs could also be employed. For example,the monetary costs could include hourly rates of technicians thatmaintain nodes 210. The geo-political regions can also vary widely.Example geo-political regions include a country, a state, a province, acounty, a city, a town, a village, a zip code, a precinct, aneighborhood, or other forms of political or geographic regions.

Differences among fabric metrics can exist at different points in timeas well as at different locations in space (e.g., geographic location,surface versus orbit, etc.). Temporal differences in metrics can alsogive rise to optimization. Referencing back to the power consumptioncosts, a power cost metric could fluctuate by hour across a regionaccording to a known pattern. Such historical knowledge can be broughtto bear against developing corresponding routing policies. Thehistorical information or pattern information can be a priori providedto the system or could be detected through observation by fabric manager220. Thus, the prioritized routing policies can be based on temporaldifferences of fabric awareness metrics where preferred routes orprioritized route criteria fluctuate with time in a manner that reflectsthe temporal aspects of the corresponding metrics. For example,preferred routes can shift paths or the ranking of preferred routes canchange to according to temporal rules.

Considering that fabric metrics can vary with time in expected orunexpected ways, the fabric awareness module within nodes 210 canobserve or otherwise monitor changes in the fabric environmentalmetrics. The awareness module can observe internal metrics, localexternal metrics, region metrics, or even global fabric metrics. Upondetecting a change in a salient metric associated with tables 230 orcorresponding routing policies, the awareness module possibly withinfabric manager 220 can update corresponding routing policies to reflectthe change to the changed metric. Further, fabric manager 220 can thenconstruct updated fabric awareness tables 230 and disseminate thetables, or changes to tables 230, to nodes 210.

Network nodes 210 are presented as active devices capable of makingrouting decisions for packets or other types of data traffic. It is alsocontemplated that fabric 240 can include other types of nodes beyondactive devices. In some embodiments, networking nodes 210 can includepassive devices that do not make active decisions with respect torouting traffic. For example, a passive node could include a repeater(e.g., wireless repeater, an optical fiber repeater, etc.) that merelyensures signals are propagated correctly. In view that a passive nodecan also be part of the fabric and can impact metrics, the passive nodescan also supply fabric awareness information or metrics that can be usedto generate routing policies for the whole fabric.

Yet another interesting aspect of the disclosed subject matter is thatoptimization occurs at various scales of the fabric. As a concreteexample fabric 240 could include the National Lamba Rail (NLR; seewww.nlr.net). Consider a fabric management interface capable of view astatus of entire NLR at various scales. The management interface couldviewing the entire NLR and provide a power cost metric for the entireNLR under its current routing configuration. As a user “zooms” into amore regional view, the total power cost metric can shift or bede-emphasized as the region's cost metric becomes more visible. Furtherzooming into the smaller scales, can give rise to finer granularity ofdata.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the spirit of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification claims refers to at leastone of something selected from the group consisting of A, B, C . . . andN, the text should be interpreted as requiring only one element from thegroup, not A plus N, or B plus N, etc.

1-19. (canceled)
 20. A networking node in a networking environment, thenetworking node comprising: at least one communication port; anenvironment sensor interface configured to acquire fabric environmentsensor data and power consumption data from a local environment in whichthe networking node is located, and further configured to combine thefabric environment sensor data and the power consumption data intofabric awareness information, the fabric awareness information includingenvironment sensor data for at least one other networking node of adistributed computation fabric including the networking node and the atleast one other networking node, wherein each networking node isconfigured to support one or more distributed applications, and whereinthe fabric awareness information comprises monetary costs of distributedcomputation; and a policy generator, coupled with the environment sensorinterface, configured to generate a computation topology according to atleast the fabric awareness information, wherein the computation topologyoptimizes one or more fabric environment metrics of the fabric awarenessinformation to align with the one or more distributed applications. 21.The networking node of claim 20, wherein the at least one communicationport comprises multiple logical ports including addressable channels,and the multiple logical ports are addressable via at least one of awavelength, a frequency, a signature, and a port identifier.
 22. Thenetworking node of claim 20, wherein the networking node is configuredto operate as at least one of a switch, a router, an access point, agateway, a firewall, a proxy, and a name server.
 23. The networking nodeof claim 20, wherein the distributed computation fabric includes thenetworking node and other fungible nodes.
 24. The networking node ofclaim 20, wherein the fabric environment sensor data comprises locationinformation of at least one of the networking node and the at least oneother networking node.
 25. The networking node of claim 20, wherein theenvironment sensor interface is further configured to convert the fabricenvironment sensor data into local fabric environment metrics.
 26. Thenetworking node of claim 20, wherein the environment sensor interface isfurther configured to transmit local fabric environment metrics to othernetworking nodes of the distributed computation fabric.
 27. Thenetworking node of claim 20, wherein the fabric awareness informationcomprises geographic region monetary costs of at least one resource inat least one geographic region.
 28. The networking node of claim 27,wherein the geographic region monetary costs comprise energy costs. 29.The networking node of claim 27, wherein the geographic region monetarycosts comprise costs from at least one of an area, a country, a state, aprovince, a county, a city, a town, and a village.
 30. The networkingnode of claim 20, wherein the policy generator is configured to updatethe computation topology in real-time with respect to a change in thefabric awareness information.
 31. The networking node of claim 20,wherein the computation topology is generated based on a cost of powerbalanced against quality-of-service.
 32. The networking node of claim20, wherein the one or more distributed applications of the distributedcomputation fabric include executing one or more cryptographicfunctions.
 33. The networking node of claim 20, wherein the one or morecryptographic functions include block chain functions.
 34. A networkingsystem, comprising: a distributed computation fabric including a firstnetworking node and a second networking node, wherein each networkingnode is configured to support one or more distributed applications; anenvironment sensor interface configured to acquire fabric environmentsensor data and node data from a local environment in which the firstnetworking node is located, and further configured to combine the fabricenvironment sensor data and the node data into fabric awarenessinformation, the fabric awareness information including environmentsensor data for second networking node, wherein the fabric awarenessinformation comprises monetary costs of distributed computation; and apolicy generator, coupled with the environment sensor interface,configured to generate a computation topology according to at least thefabric awareness information, wherein the computation topology optimizesone or more fabric environment metrics of the fabric awarenessinformation to align with the one or more distributed applications. 35.The system of claim 34, wherein the fabric environment sensor datacomprises location information of at least one of the first networkingnode and the second networking node.
 36. The system of claim 34, whereinthe environment sensor interface is further configured to transmit localfabric environment metrics to other networking nodes of the distributedcomputation fabric.
 37. The system of claim 34, wherein the fabricawareness information comprises geographic region monetary costs of atleast one resource in at least one geographic region.
 38. The system ofclaim 34, wherein the policy generator is configured to update thecomputation topology in real-time with respect to a change in the fabricawareness information.
 39. A method of generating a computation topologyin a distributed computation fabric, the method comprising: acquiring,at an environment sensor interface of a first networking node in thedistributed computation fabric, fabric environment sensor data and powerconsumption data from a local environment in which the first networkingnode is located; combining the fabric environment sensor data and thepower consumption data into fabric awareness information, the fabricawareness information including environment sensor data for at least asecond networking node of the distributed computation fabric, whereineach networking node is configured to support one or more distributedapplications, and wherein the fabric awareness information comprisesmonetary costs of distributed computation; and generating, by a policygenerator coupled with the environment sensor interface, a computationtopology according to at least the fabric awareness information, whereinthe computation topology optimizes one or more fabric environmentmetrics of the fabric awareness information to align with the one ormore distributed applications.